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| Paper Title: | SURVEY ON: MACHINE LEARNING BASED SYSTEM TO PREDICTING STOCK MARKET TRENDS VIA CONTINUES AND BINARY DATA |
| Authors Name: | Jamdhade Akshada , Jadhav Priyanka , Jagzap Payal , Thakre Priyanka , Prof.Chandgude.A.S |
| Unique Id: | IJSDR2201025 |
| Published In: | Volume 7 Issue 1, January-2022 |
| Abstract: | The main objective of this project is to find the best model to predict the value of the stock market. During the process of considering various techniques and variables that must be taken into account, we found out that techniques like random forest, support vector machine were not exploited fully. In, this project we are going to present and review a more feasible method to predict the stock Movement with higher accuracy. The first thing we have taken into account is the dataset of the Stock market prices from previous year. The dataset was preprocessed and tuned up for real analysis. Hence, our project will also focus on data pre-processing of the raw dataset. Secondly, after preprocessing the data, we will review the use of random forest, support vector machine on the dataset and the outcomes it generates. In addition, the proposed project examines the use of the prediction System In real-world settings and issues associated with the accuracy of the overall values given. The Project Also presents a machine-learning model to predict the longevity of stock in a competitive market. The successful prediction of the stock will be a great asset for the stock market institutions and will provide Real-life solutions to the problems that stock investors face. |
| Keywords: | Stocks, Machine Learning, Processing, Dataset, Support Vector Machine, Database, Investor. |
| Cite Article: | "SURVEY ON: MACHINE LEARNING BASED SYSTEM TO PREDICTING STOCK MARKET TRENDS VIA CONTINUES AND BINARY DATA", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 1, page no.192 - 194, January-2022, Available :http://www.ijsdr.org/papers/IJSDR2201025.pdf |
| Downloads: | 000223232 |
| Publication Details: | Published Paper ID: IJSDR2201025 Registration ID:193878 Published In: Volume 7 Issue 1, January-2022 DOI (Digital Object Identifier): Page No: 192 - 194 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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